Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory89.0 B

Variable types

Numeric2
Categorical3
DateTime2
Text3

Dataset

Description경상남도 창원시의 EGS전자예금압류 현황 시스템의 체납 현황입니다. 항목은 순번, 자치단체코드, 체납자구분, 압류등록일, 취소일, 압류본세, 압류가산금, 압류건수, 추심최소요청금액, 은행명입니다.
Author경상남도 창원시
URLhttps://www.data.go.kr/data/15091576/fileData.do

Alerts

자치단체코드 has constant value ""Constant
압류건수 is highly overall correlated with 체납자구분High correlation
체납자구분 is highly overall correlated with 압류건수 and 1 other fieldsHigh correlation
은행명 is highly overall correlated with 체납자구분High correlation
체납자구분 is highly imbalanced (56.1%)Imbalance
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:21:11.989464
Analysis finished2023-12-12 02:21:12.863598
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:21:12.918176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-12T11:21:13.033965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
48120
22 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48120
2nd row48120
3rd row48120
4th row48120
5th row48120

Common Values

ValueCountFrequency (%)
48120 22
100.0%

Length

2023-12-12T11:21:13.148688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:21:13.256841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48120 22
100.0%

체납자구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
법인
20 
개인
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row법인
2nd row법인
3rd row법인
4th row법인
5th row법인

Common Values

ValueCountFrequency (%)
법인 20
90.9%
개인 2
 
9.1%

Length

2023-12-12T11:21:13.363339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:21:13.479285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 20
90.9%
개인 2
 
9.1%

등록일
Date

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2021-02-08 00:00:00
Maximum2021-07-13 00:00:00
2023-12-12T11:21:13.566678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:21:13.700079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

등록취소일
Date

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2021-02-24 00:00:00
Maximum2021-07-26 00:00:00
2023-12-12T11:21:13.819506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:21:13.940356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

압류본세
Text

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T11:21:14.106234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.4090909
Min length9

Characters and Unicode

Total characters207
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)72.7%

Sample

1st row 34,198,380
2nd row 125,000
3rd row 118,950
4th row 356,420
5th row 575,250
ValueCountFrequency (%)
136,710 2
 
9.1%
110,380 2
 
9.1%
145,210 2
 
9.1%
204,310 1
 
4.5%
34,198,380 1
 
4.5%
16,139,470 1
 
4.5%
156,250 1
 
4.5%
320,610 1
 
4.5%
212,660 1
 
4.5%
628,250 1
 
4.5%
Other values (9) 9
40.9%
2023-12-12T11:21:14.465821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
21.3%
0 32
15.5%
1 27
13.0%
, 25
12.1%
2 16
 
7.7%
5 12
 
5.8%
3 11
 
5.3%
6 11
 
5.3%
4 9
 
4.3%
8 9
 
4.3%
Other values (2) 11
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 138
66.7%
Space Separator 44
 
21.3%
Other Punctuation 25
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
23.2%
1 27
19.6%
2 16
11.6%
5 12
 
8.7%
3 11
 
8.0%
6 11
 
8.0%
4 9
 
6.5%
8 9
 
6.5%
7 7
 
5.1%
9 4
 
2.9%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
44
21.3%
0 32
15.5%
1 27
13.0%
, 25
12.1%
2 16
 
7.7%
5 12
 
5.8%
3 11
 
5.3%
6 11
 
5.3%
4 9
 
4.3%
8 9
 
4.3%
Other values (2) 11
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
21.3%
0 32
15.5%
1 27
13.0%
, 25
12.1%
2 16
 
7.7%
5 12
 
5.8%
3 11
 
5.3%
6 11
 
5.3%
4 9
 
4.3%
8 9
 
4.3%
Other values (2) 11
 
5.3%

압류가산금
Text

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T11:21:14.655229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.8636364
Min length7

Characters and Unicode

Total characters173
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)72.7%

Sample

1st row 2,296,590
2nd row 3,740
3rd row 3,560
4th row 10,680
5th row 17,240
ValueCountFrequency (%)
4,100 2
 
9.1%
3,300 2
 
9.1%
5,060 2
 
9.1%
6,110 1
 
4.5%
2,296,590 1
 
4.5%
1,603,090 1
 
4.5%
4,670 1
 
4.5%
126,840 1
 
4.5%
6,360 1
 
4.5%
18,960 1
 
4.5%
Other values (9) 9
40.9%
2023-12-12T11:21:15.010711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
25.4%
0 33
19.1%
, 25
14.5%
6 13
 
7.5%
1 11
 
6.4%
4 9
 
5.2%
3 9
 
5.2%
2 8
 
4.6%
5 7
 
4.0%
8 5
 
2.9%
Other values (2) 9
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104
60.1%
Space Separator 44
25.4%
Other Punctuation 25
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
31.7%
6 13
 
12.5%
1 11
 
10.6%
4 9
 
8.7%
3 9
 
8.7%
2 8
 
7.7%
5 7
 
6.7%
8 5
 
4.8%
9 5
 
4.8%
7 4
 
3.8%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
44
25.4%
0 33
19.1%
, 25
14.5%
6 13
 
7.5%
1 11
 
6.4%
4 9
 
5.2%
3 9
 
5.2%
2 8
 
4.6%
5 7
 
4.0%
8 5
 
2.9%
Other values (2) 9
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
25.4%
0 33
19.1%
, 25
14.5%
6 13
 
7.5%
1 11
 
6.4%
4 9
 
5.2%
3 9
 
5.2%
2 8
 
4.6%
5 7
 
4.0%
8 5
 
2.9%
Other values (2) 9
 
5.2%

압류건수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3636364
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:21:15.141334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q34
95-th percentile8.95
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0635407
Coefficient of variation (CV)0.91078236
Kurtosis3.9577204
Mean3.3636364
Median Absolute Deviation (MAD)1.5
Skewness1.9601442
Sum74
Variance9.3852814
MonotonicityNot monotonic
2023-12-12T11:21:15.246196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 7
31.8%
3 4
18.2%
2 4
18.2%
4 3
13.6%
13 1
 
4.5%
9 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
ValueCountFrequency (%)
1 7
31.8%
2 4
18.2%
3 4
18.2%
4 3
13.6%
5 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
13 1
 
4.5%
ValueCountFrequency (%)
13 1
 
4.5%
9 1
 
4.5%
8 1
 
4.5%
5 1
 
4.5%
4 3
13.6%
3 4
18.2%
2 4
18.2%
1 7
31.8%

추심최소요청금액
Text

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T11:21:15.466605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1818182
Min length7

Characters and Unicode

Total characters180
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)72.7%

Sample

1st row 400,790
2nd row 84,850
3rd row 58,140
4th row 367,100
5th row 2,220
ValueCountFrequency (%)
35,810 2
 
9.1%
113,680 2
 
9.1%
23,610 2
 
9.1%
25,960 1
 
4.5%
400,790 1
 
4.5%
25,740 1
 
4.5%
160,920 1
 
4.5%
83,720 1
 
4.5%
88,400 1
 
4.5%
4,840 1
 
4.5%
Other values (9) 9
40.9%
2023-12-12T11:21:15.889596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
24.4%
0 34
18.9%
, 22
12.2%
1 17
 
9.4%
8 11
 
6.1%
3 10
 
5.6%
2 10
 
5.6%
4 9
 
5.0%
6 8
 
4.4%
5 7
 
3.9%
Other values (2) 8
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114
63.3%
Space Separator 44
 
24.4%
Other Punctuation 22
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34
29.8%
1 17
14.9%
8 11
 
9.6%
3 10
 
8.8%
2 10
 
8.8%
4 9
 
7.9%
6 8
 
7.0%
5 7
 
6.1%
7 4
 
3.5%
9 4
 
3.5%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
44
24.4%
0 34
18.9%
, 22
12.2%
1 17
 
9.4%
8 11
 
6.1%
3 10
 
5.6%
2 10
 
5.6%
4 9
 
5.0%
6 8
 
4.4%
5 7
 
3.9%
Other values (2) 8
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
24.4%
0 34
18.9%
, 22
12.2%
1 17
 
9.4%
8 11
 
6.1%
3 10
 
5.6%
2 10
 
5.6%
4 9
 
5.0%
6 8
 
4.4%
5 7
 
3.9%
Other values (2) 8
 
4.4%

은행명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size308.0 B
경남은행
9 
기업은행
4 
농협은행
3 
KEB하나은행
2 
국민은행
2 
Other values (2)
2 

Length

Max length7
Median length4
Mean length4.3636364
Min length4

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row경남은행
2nd row경남은행
3rd row기업은행
4th row경남은행
5th row경남은행

Common Values

ValueCountFrequency (%)
경남은행 9
40.9%
기업은행 4
18.2%
농협은행 3
 
13.6%
KEB하나은행 2
 
9.1%
국민은행 2
 
9.1%
신한은행 1
 
4.5%
농협본부총괄 1
 
4.5%

Length

2023-12-12T11:21:16.096261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:21:16.261312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경남은행 9
40.9%
기업은행 4
18.2%
농협은행 3
 
13.6%
keb하나은행 2
 
9.1%
국민은행 2
 
9.1%
신한은행 1
 
4.5%
농협본부총괄 1
 
4.5%

Interactions

2023-12-12T11:21:12.438378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:21:12.284148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:21:12.530483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:21:12.355772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:21:16.368816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번체납자구분등록일등록취소일압류본세압류가산금압류건수추심최소요청금액은행명
순번1.0000.7800.5490.0000.8990.8990.3640.8990.000
체납자구분0.7801.0001.0001.0001.0001.0000.6111.0000.728
등록일0.5491.0001.0000.9321.0001.0000.8371.0000.403
등록취소일0.0001.0000.9321.0000.9830.9830.8050.9830.605
압류본세0.8991.0001.0000.9831.0001.0001.0001.0000.000
압류가산금0.8991.0001.0000.9831.0001.0001.0001.0000.000
압류건수0.3640.6110.8370.8051.0001.0001.0001.0000.693
추심최소요청금액0.8991.0001.0000.9831.0001.0001.0001.0000.000
은행명0.0000.7280.4030.6050.0000.0000.6930.0001.000
2023-12-12T11:21:16.493677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행명체납자구분
은행명1.0000.679
체납자구분0.6791.000
2023-12-12T11:21:16.605224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번압류건수체납자구분은행명
순번1.0000.1040.4640.000
압류건수0.1041.0000.5630.276
체납자구분0.4640.5631.0000.679
은행명0.0000.2760.6791.000

Missing values

2023-12-12T11:21:12.659974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:21:12.810132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

순번자치단체코드체납자구분등록일등록취소일압류본세압류가산금압류건수추심최소요청금액은행명
0148120법인2021-02-152021-03-1834,198,3802,296,5903400,790경남은행
1248120법인2021-03-102021-03-23125,0003,740284,850경남은행
2348120법인2021-03-102021-03-31118,9503,560258,140기업은행
3448120법인2021-03-162021-04-14356,42010,6801367,100경남은행
4548120법인2021-03-162021-04-14575,25017,240132,220경남은행
5648120법인2021-03-102021-03-31107,1203,20041,160경남은행
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